MAJOR Warning for Software Stocks... (The Truth Exposed)

Today marks the biggest software selloff we have seen in about 2 months and I'm going to tell you exactly why that is happening in the next 10 minutes. We just got a massive warning for the AI trade and it came from Pegas and what they highlight needs to be talked about. We do have earnings coming out here in just the next couple of hours for Tesla and Alphabet and Service Now and official numbers from IBM and Texas Instruments. Tomorrow morning, you have Nokia and Intel and this information we're going to talk about in today's episode could not come at a better time. You have to understand this. And I think I found a new stock that looks very attractive and we'll talk about that as well in this video. So ladies and gentlemen, I don't want to waste your time at all. The only thing that I ask you to do is hit that like button, subscribe to the channel if you guys find value out of today's episode because doing those things helps push this out to more people that need to be informed at a moment like this. Okay, so first things first, what you're going to notice is yes, AI stocks are moving higher today. Other areas are doing okay. some areas of healthcare, industrials, energy and oil, AI stocks generally. Microsoft down 2% today, Meta's down 2 and a.5%. Amazon down almost 2% today, you know, Tesla not doing great either. Palanteer down over 4% today, and software across the board getting beaten up. So what I'm trying to highlight here is it's not just software like smaller software companies. It is even your trillion plus dollar stocks Microsoft Meta and Amazon and even Tesla that are selling off on this warning. So Pegas reported earnings coming out with some disappointing Q2 results. The company cited widespread quote AI market confusion and macro shifts which caused large enterprise clients to delay or elongate their purchasing decisions. EPS missed by about 20%. Revenue came in at about 9.4% at 420.72 million. Wall Street was expecting 427.38 million. So it wasn't even that big of a miss. But again, if you're a software stock and you're missing, that's not great. Total ACV, annual contract value, slowed significantly to 7% year-over-year, 8% in a constant currency and down 12% from the prior quarter. There was some bright spots like the Peggloud ACV, which expanded 22% year-over-year. This is their cloud division now making up 57% of total ACV mix. They do have strong free cash flow generations, 288 million. Financially, the company is is just fine. But again, CEO Alan Trefler noted that enterprise customers are hyperfocused on restructuring their AI strategies, causing them to temporarily pause broader cloud workflow commitments. Pegasus management reported that slowing AI adoption is driven by AI chaos, quote, AI chaos, including customer confusion over build versus buy decisions, fatigue over unpredictable token pricing and elongated sales cycles. Additionally, leadership admitted to internal execution failures in their go-to market strategy that compounded the macroeconomic slowdown. But there is a slowdown in AI adoption. And I've talked about this many times on the channel. It's not to be feared. It is actually your opportunity. See, there are massive bottlenecks to companies actually adopting AI. The technology is there. It is useful. It it is great. Much like the internet in the early days of the internet, it took a very long time for companies to actually adopt the internet in a meaningful way. It took 15 years since, you know, the birth of the internet, which was 1983 or so until there was actually mass adoption in the late 1990s. Really from like 1995 through 2000 is when it became standard to have even a web page, right? It's not like these companies can go out and just adopt AI. There's a lot of bottlenecks. Number one is leadership and uh strategy gaps. Okay. So only 3% of organizations believe their leaders are fully prepared to manage AI enabled ways of thinking. There's not a lot of uniformed visions around AI. Number two, workforce readiness and friction. Companies often rush to buy software without building internal AI fluency. Kind of like the early days of the internet as well. You know, people went out, they bought computers, they started experimenting with computers, but they didn't really know how to implement them to actually be productive. But the biggest problem is actually from a data perspective. So data infrastructure realities, dirty data and silo data. AI requires structured, clean, and highly accessible data. Okay. A it says a Kaufman Rosson mid-market report highlighted that 45% of manufacturers still have information completely siloed across disconnected systems, trapping 73% of these firms in an endless testing phase. And this data silo problem is why 73% of companies remain permanently stuck in the AI prototyping phase. You can't even implement AI if you wanted to. And that's why companies like Data Dog and Snowflake are seeing such incredible demand because these companies have to restructure their data in a uniform way so AI can use it, right? And what unstructured data really means is you have text data next to audio data next to video data just thrown into a data warehouse, right? All stored together. You have to structure that and give it some context before AI can use it. So Snowflake, Data Dog, they are big early winners from this theme of companies restructuring their data and sending it to data lakes. Now back in 1987, Robert Solo Solo famously quipped, quote, you can see the computer age everywhere, but in the product but in the productivity statistics. This is the Solo's paradox. But Solo's paradox holds that major new technologies can spread quickly while measure productivity barely moves because businesses first need to reorganize their workflows, systems, and people around them. It happened with electricity. It happened with the internet. Now it's happening with AI and automation. While AI and automation are spreading quickly, enterprises haven't fully reorganized themselves around them. Good news is that the Solo's paradox is usually a lag, not a permanent failure. In the mid1 1990s, nearly a decade after Solo made his remark, the economy finally reorganized around computers, causing national productivity growth to suddenly double in a hockey stick-like spike. Economists predict AI will eventually do the same once businesses finish the painful work of cleaning data and restructuring human roles, which you can see by this image on screen. How the adoption curve is actually going to go with AI. These are AI agents, right? This is a chart of AI agent deployments. 2026, you're seeing nothing right now. Like almost nothing. less way less than even a 100red million AI agents out there being used in operations. You are right here. Okay. Well, what you're going to see happen is next year AI is going to begin to ramp, right? The people that are restructuring their data right now, they're going to start to be able to implement AI next year. But the acceleration really doesn't even happen in the Scurve until 2028. 2028 is when you go from about 500 million AI agents being used in companies operations all the way up to about 2.5 billion AI agents being used by 2030. This is the Scurve. Now, Wall Street, they priced these things in ahead of time, right? So by next year as you start to see this adoption really picking up a bit compared to 2026 you're going to see people go oh wait you know software they are going to be big winners there will be this mass enterprise adoption but there's these bottlenecks in the meantime that need to be resolved also tell you that this brings up a problem potentially for the capex trade because if you're going to go through this air pocket let's say over the next year or though and companies have to restructure data in order to even use AI. Well, doesn't that mean we might have a slowdown in compute demand that maybe the capex trade could actually slow down a little bit? Because the main drivers that will actually use compute are mass enterprise adoption, which really doesn't even happen until 2028 through 2030 and then beyond, and then robotics, which are probably a handful of years away. So, as companies are expected to double down on spending next year, you know, Google's expected to spend 50 to 75% more next year than they're going to spend this year, going from about $200 billion this year all the way up to potentially $350 billion next year. Do you have to spend that aggressively? If we're going through this compute air pocket where companies, they're just not massimplementing AI at this moment. I think it would argue that capex could slow down and that's something we need to be prepared for. Now, all of this to a newer investor might sound like doom and gloom. It's not. It's quite the opposite. You have a once-in-a-lifetime opportunity because markets and Wall Street, they're all gamblers. They're all these shortterm mindset perspectives. They want to show, you know, returns on their portfolio for their clients this quarter and next quarter. But if you can see out past the see on the horizon, you're going to notice, yes, mass adoption of AI is coming. It is a reality. There's a little bit of a bottleneck. There's a speed bump. Got to slow down a little bit to actually do this thing right. But these companies, these software companies, they are going to be massive AI winners from this. And no, they're not going to be massive AI winners next quarter, but next year, that's when Wall Street's going to start to see, oh, wait, yeah, this mass enterprise adoption of AI is actually coming. And to some degree, if you're going to say software is a loser from AI, then you're also going to say AI in general is a bubble, right? You can't have software lose from AI and still be bullish on the hardware trade. They kind of go hand in hand because Alex Karp and Palanteer CEO is correct. If you are actively partnering with large language models just on a first party proprietary basis, right? If I go to a large language model company and say, "Hey, let's partner." and I start feeding my data into a large language model. That data is being distilled and those large language models are stealing my alpha, stealing my intellectual property. That doesn't work. That's not going to happen. Software is critical to actually making AI useful and safe for companies. Now, the stock that I actually think looks attractive now is Pega Systems. And I own this thing, you know, like a year or two ago. did well, made some money, sold out, but the stock has come down a lot. And following today, stocks down like 15%. Market cap sits at about 4.5 billion. The their year-to- date decline is down 48%. Their trailing PE ratio is at 14.4x. They have 75% plus gross margins. their forward PE ratio is down to 9 and a half which that is just insane. Now the company uh in the latest quarter had 288 million in free cash flow. Okay well in the in the first half of 2026 alone. Pegas is trading at a forward price of sales multiple of just 2.2x. And really this is one of the reasons I I just love software in general. Not all of them, but some software stocks are going to be big winners. The Enterprise Software Pure Group average trades on a forward PE average of 12.4x. 12.4x. The S&P sitting at over 20 today. And these are going to be big AI winners. But Wall Street, they're impatient. They don't see that right now. They don't even care about that right now. every hedge fund manager on Wall Street, institutional investor, they're just trying to make money for their clients right now. So, as things like the AI trade, the hardware trade is, you know, seemingly alive and well, people are rushing into those stocks. But that's quite the opposite of what you want to do. You want to avoid those stocks like the plague and be positioning into these companies with a forward-looking mentality for the next 18 to 24 months and saying, "Hey, what's going to happen with AI adoption over the next 18 to 24 months?" And when you do that, you realize these are some of the biggest opportunities we've ever seen. In fact, Stanley Drunken Miller, one of the best investors to ever do this, says, quote, "Never invest in the present. Always try and envision the situation as you see it in 18 to 24 months. Stanley Drunken Miller argues that the biggest mistake novice investors make is focusing entirely on what a company or the economy looks like today. In his view, current market conditions are already priced into securities. Meaning, you can only make money on looking at where the puck is going instead of where it currently rests. That's exactly what everyone on Wall Street does today. And this is why people are never early to big opportunities in the trading community. This is what we do. We are solely envisioning what companies are going to look like, what the you know sentiment around markets is going to look like, where new technologies are going to be in the next 1 to 3 years and positioning aggressively into those winners. And right now the winners are going to be the stocks that are trading at 12 times forward pees. These stocks are multibaggers in my opinion. Obviously not a financial adviser once mass enterprise AI adoption actually takes place. But again, one of the biggest bottlenecks to actually getting this mass adoption of AI is the data sitting in data silos. Right? That's why a snowflake and a data dog are seeing such strong demand right now because this is a problem. But it's not a unsolvable problem. It's not a problem that will be with us forever. It's a problem that is actively being worked through right now. So for software stocks, some of the best software stocks out there right now I think are Zeta Global, Rubric, UiPath, Service Now, Zcaler, HubSpot, MongoDB, Snowflake, Data Dog, Back Blaze, Pegasus. And again, we are going to have earnings from uh Service Now today, just in the next couple of hours. And I'm gonna cover that on this channel. We're gonna talk about Alphabet. We're gonna talk about Service Now and all of the earnings that come out. But it's bigger than that. I don't give a what Service Now says. In all reality, we're positioning for the next couple of years and the outperformance that we're going to see. Now, ladies and gentlemen, that is going to do it for today's episode. Hit that like button if you guys learned something for the YouTube algorithm so everyone can hear this because I think everyone needs to hear this if you're looking for the next multibagger opportunity. Hit that subscribe button as well if you guys made it to the end of this video. Have a fantastic rest of your day and I will see you in the next